385 416
Full Length Article
Journal of Artificial Intelligence and Metaheuristics
Volume 3 , Issue 2, PP: 18-27 , 2023 | Cite this article as | XML | Html |PDF

Title

A Deep Learning Approach Visual Recognition of Bird Species in Noisy Environments

  P. K. Duta 1 * ,   Nader Behdad 2

1  School of Engineering and Technology, Amity University Kolkata,India
    (pkdutta@kol.amity.edu)

2  Electrical and Computer Engineering , The Polytechnic University of the Philippines, Manila, 1016, Philippines
    (ohowpy@gmail.com)


Doi   :   https://doi.org/10.54216/JAIM.030202

Received: August 14, 2022 Revised: November 11, 2022 Accepted: March 18, 2023

Abstract :

In this paper, we propose a deep learning approach for visual recognition of bird species in noisy environments. Bird species recognition has been a challenging task due to the high variation in bird appearances and the presence of noise and clutter in natural environments. Our approach utilizes a deep convolutional neural network (CNN) to learn discriminative features from bird images and classify them into different species. We also incorporate data augmentation techniques to increase the diversity of the training data and improve the robustness of the model. To address the issue of noisy environments, we introduce a novel noise-robust loss function that penalizes the model for incorrect predictions caused by noise. We evaluate our approach on a dataset of bird images collected from diverse environments and compare it with state-of-the-art methods. Our results demonstrate that our approach achieves superior performance in both clean and noisy environments, highlighting the effectiveness of our noise-robust loss function. Our approach has the potential to be applied in real-world scenarios for bird species recognition and conservation.

Keywords :

Machine Learning; Visual Recognition; Bird Species Classification; Deep Learning

References :

[1] Chen Chaofan, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, and Jonathan K. Su., This looks like that: deep learning for interpretable image recognition. Advances in neural information processing systems, 32, 2019.

[2] Jacob I. Jeena, and P. Ebby Darney, Design of deep learning algorithm for IoT application by image based recognition. Journal of ISMAC 3, 03, 276-290, 2021.

[3] Nauta, Meike, Ron Van Bree, and Christin Seifert, Neural prototype trees for interpretable fine-grained image recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 14933-14943, 2021.

[4] Ding W., Abdel-Basset M., Hawash H., Pedrycz W., Multimodal infant brain segmentation by fuzzy-informed deep learning. IEEE Transactions on Fuzzy Systems, 30(4), 1088-1101, 2021.

[5] Huang Yo-Ping, and Haobijam Basanta, Bird image retrieval and recognition using a deep learning platform. IEEE Access, 7, 66980-66989, 2019.

[6] Wei, Xiu-Shen, Jianxin Wu, and Quan Cui, Deep learning for fine-grained image analysis: A survey. arXiv preprint arXiv:1907, 03069, 2019.

[7] Wei, Xiu-Shen, Yi-Zhe Song, Oisin Mac Aodha, Jianxin Wu, Yuxin Peng, Jinhui Tang, Jian Yang, and Serge Belongie, Fine-grained image analysis with deep learning: A survey. IEEE transactions on pattern analysis and machine intelligence 44(12), 8927-8948, 2021.

[8] Pan, Mingyang, Yisai Liu, Jiayi Cao, Yu Li, Chao Li, and Chi-Hua Chen, Visual recognition based on deep learning for navigation mark classification. IEEE Access, 8, 32767-32775, 2020.

[9] Rashid Muhammad, Muhammad Attique Khan, Majed Alhaisoni, Shui-Hua Wang, Syed Rameez Naqvi, Amjad Rehman, and Tanzila Saba, A sustainable deep learning framework for object recognition using multi-layers deep features fusion and selection. Sustainability, 12 (12), 5037, 2020.

[10] Xie Jie, and Mingying Zhu, Handcrafted features and late fusion with deep learning for bird sound classification. Ecological Informatics, 52, 74-81, 2019.

[11] Ferreira André C., Liliana R. Silva, Francesco Renna, Hanja B. Brandl, Julien P. Renoult, Damien R. Farine, Rita Covas, and Claire Doutrelant, Deep learning‐based methods for individual recognition in small birds. Methods in Ecology and Evolution, 11(9), 1072-1085, 2020.

[12] Akçay H. G., Kabasakal B., Aksu D., Demir N. Öz M., & Erdoğan, A., Automated bird counting with deep learning for regional bird distribution mapping. Animals, 10(7), 1207, 2020.

[13] Kahl S., Wood C. M., Eibl M., & Klinck H., BirdNET: A deep learning solution for avian diversity monitoring. Ecological Informatics, 61, 101236, 2021.

[14] Abdel-Basset M., Hawash H., Moustafa N., & Mohammad N., H2HI-Net: A Dual-Branch Network for Recognizing Human-to-Human Interactions From Channel-State Information. IEEE Internet of Things Journal, 9(12), 10010-10021, 2021.

[15] Islam S., Khan S. I. A., Abedin M. M., Habibullah K. M., & Das, A. K., Bird species classification from an image using VGG-16 network. In Proceedings of the 7th International Conference on Computer and Communications Management, 38-42, 2019.

[16] Mohanty Ricky, Bandi Kumar Mallik, and Sandeep Singh Solanki, Automatic bird species recognition system using neural network based on spike. Applied Acoustics 161,107177, 2020.


Cite this Article as :
Style #
MLA P. K. Duta, Nader Behdad. "A Deep Learning Approach Visual Recognition of Bird Species in Noisy Environments." Journal of Artificial Intelligence and Metaheuristics, Vol. 3, No. 2, 2023 ,PP. 18-27 (Doi   :  https://doi.org/10.54216/JAIM.030202)
APA P. K. Duta, Nader Behdad. (2023). A Deep Learning Approach Visual Recognition of Bird Species in Noisy Environments. Journal of Journal of Artificial Intelligence and Metaheuristics, 3 ( 2 ), 18-27 (Doi   :  https://doi.org/10.54216/JAIM.030202)
Chicago P. K. Duta, Nader Behdad. "A Deep Learning Approach Visual Recognition of Bird Species in Noisy Environments." Journal of Journal of Artificial Intelligence and Metaheuristics, 3 no. 2 (2023): 18-27 (Doi   :  https://doi.org/10.54216/JAIM.030202)
Harvard P. K. Duta, Nader Behdad. (2023). A Deep Learning Approach Visual Recognition of Bird Species in Noisy Environments. Journal of Journal of Artificial Intelligence and Metaheuristics, 3 ( 2 ), 18-27 (Doi   :  https://doi.org/10.54216/JAIM.030202)
Vancouver P. K. Duta, Nader Behdad. A Deep Learning Approach Visual Recognition of Bird Species in Noisy Environments. Journal of Journal of Artificial Intelligence and Metaheuristics, (2023); 3 ( 2 ): 18-27 (Doi   :  https://doi.org/10.54216/JAIM.030202)
IEEE P. K. Duta, Nader Behdad, A Deep Learning Approach Visual Recognition of Bird Species in Noisy Environments, Journal of Journal of Artificial Intelligence and Metaheuristics, Vol. 3 , No. 2 , (2023) : 18-27 (Doi   :  https://doi.org/10.54216/JAIM.030202)